People have always worked short term jobs or “gigs.” But with the rise of mobile devices, companies can connect to workers looking for gigs — and vice versa — like never before.
Wharton Prof. Gad Allon researched how workers in the gig economy make labor decisions to better understand how the platforms influence behavior.
Companies like Uber and DoorDash compete not only for customers, but for the same pool of workers who split their time between multiple services.
“For many of us, when we make labor decisions, we make it once a year, twice a year, or maybe once in seven years,” Allon said. “Here, we talk about people who make decisions by the minute. And when they make decisions by the minute, they make the decision on where to work, how long to work, and which firm to work (for).”
Through a study with Via, an Israeli transportation service similar to Uber, Allon studied three main factors — wages, earnings, and hours worked — to better understand why drivers decide to either stop or continue working.
“These drivers have many more opportunities, but the level of complexity increases as well. Firms have the ability to continuously cater wages and tailor incentives they offer each driver. In our data, we had 8,000 drivers, but a thousand different wages at every moment in time.”
1. Wage Per Gig: When drivers were paid more per hour, they worked more.
“Most of them are working the gig economy work because they want more flexibility. So, we see on the one side they are very rational. They are reacting to incentives the way you would expect them to.”
2. Gross Income: The more income drivers earned over time, the less likely they were to take on additional work.
“The interesting thing is that they have significant income targeting, which means the more you pay them (over time)…the less likely they were to continue (accepting shifts).”
3. How Long You’ve Been an Employee: The longer a driver has worked for Via, the more likely they were to take another shift.
“Really the surprising part is the last one, which we call inertia: the notion that the more hours they work until now, the more likely they were to continue to work,” Allon said. “[These results] have implications for how you personalize payment. For example, one experiment we plan to do with Via is how do you actually convince drivers not to work, rather than to work?”
Allon said with the emergence of more automation, he expects to see that work will be more fragmented, and more jobs will be structured like gig economy work.
It’s a good gig, if you can keep it.
— Erin Lomboy, W’21
Posted: December 3, 2019